Fairness-aware Optimization

Fairness-Aware Optimization is an optimization framework that incorporates fairness constraints or objectives into the model training process to explicitly mitigate discriminatory outcomes with respect to sensitive attributes.

178

Publications

10.9K

Citations

642

Authors

247

Institutions

Publications per year

2017–2026

160

Authors

642

  1. XW
    Xintao Wu

    University of Arkansas at Fayetteville, United States

    5 publications · 466 citations

    5

    h-index

  2. IV
    Isabel Valera

    Max Planck Institute for Software Systems, Germany

    5 publications · 1.8K citations

    5

    h-index

  3. NK
    Nisheeth K. Vishnoi

    Yale University, United States

    4 publications · 92 citations

    4

    h-index

  4. HJ
    Heinrich Jiang

    Google (United States), United States

    4 publications · 136 citations

    4

    h-index

  5. LZ
    Lu Zhang

    University of Arkansas at Fayetteville, United States

    4 publications · 450 citations

    4

    h-index

1–5 of 642

Venues

  1. arXiv (Cornell University)

    Repository · 38 publications · 2.4K citations

    23

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 315 citations

    8

    h-index

  3. IEEE Transactions on Vehicular Technology

    4 publications · 132 citations

    4

    h-index

  4. International Conference on Machine Learning

    Conference · 4 publications · 237 citations

    4

    h-index

  5. IEEE Communications Letters

    5 publications · 219 citations

    4

    h-index

1–5